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A probabilistic data stream $S$ is defined as a sequence of uncertain tuples $,i=1...\infty$, with the semantics that element $t_i$ occurs in the stream with probability $p_i \in (0,1)$. Thus each distinct element $t$, which occurs in tuples of $S$, has an existential probability based on the tuples: $ \in S$. Existing duplicate detection methods for a(More)
Approximate duplicate detection based on the Decaying Bloom Filter (DBF) for data streams over sliding windows (DDMDBF) is an effective technique, but may have a large false positive rate. Because it simply takes a querying element to be duplicated when the counters that this element is hashed to are non-zero, while neglects the actual values of the(More)
Approximate duplicate-detection (or membership query) in data streams answers the question of whether an element from a large universe U (a query element) is present in a small subsequence of a data stream or not. It is an important query that has many Internet applications, such as web crawling, social networks and so on. Existing approximate(More)
The application of plastic microfluidic chips can be extended with quartz capillaries connected at the end of their microchannels, e.g. UV absorption detection method can be carried out, which responds to almost 80% chemical compounds in detection. A vision-based experiment system for automatically assembling capillaries to plastic microfluidic chips was(More)
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